Use case

Testing AI-generated applications

AI can generate a plausible app faster than a team can specify it. The gap between plausible and correct is where bugs live, and it is best found by testing behavior rather than reading code.

Last updated October 31, 2026

Who this is for

Teams using AI coding tools and shipping generated features.

The problem

  • Generated code may handle the happy path and skip the rest.
  • Tests generated alongside code can share the same blind spots.
  • Reviewers cannot read every line at AI speed.

How Locodera handles it

  1. 01

    Test behavior

    The agent interacts with the deployed app like a user.

  2. 02

    Coverage by persona

    Different users push different expectations.

  3. 03

    Independent verification

    A separate verifier reproduces findings in a clean session.

  4. 04

    Evidence

    Screenshots, video and console/network logs support each report.

Relevant capabilities

Autonomous exploration

A real browser maps pages, forms and flows without scripts.

Six personas

New, returning, paying, frustrated, mobile and admin users.

3/3 verification

Only findings that reproduce every time are confirmed.

Regression diffs

New, resolved and returning issues per scan.

See all features →

FAQs

Can it review AI-generated code?+

No. Locodera is black-box QA; it tests the running application.

Does it catch logic bugs?+

It catches observable behavior that contradicts the UI, including state and business-logic inconsistencies.

How does it help with regressions?+

Each scan is compared with the previous one, so returning bugs and broken flows are visible.

Find the bugs your tests never looked for.

Add your app, run an exploratory scan, and get a release verdict backed by evidence.